gpd.q: Add Quantile Estimates to plot.gpd

In evir: Extreme Values in R

Description

Calculates quantile estimates and confidence intervals for high quantiles above the threshold in a GPD analysis, and adds a graphical representation to an existing plot.

Usage

gpd.q(x, pp, ci.type = c("likelihood", "wald"), ci.p = 0.95,
    like.num = 50)

Arguments

x	a list object returned by plot.gpd or tailplot
pp	the desired probability for quantile estimate (e.g. 0.99 for the 99th percentile)
ci.type	method for calculating a confidence interval: "likelihood" or "wald"
ci.p	probability for confidence interval (must be less than 0.999)
like.num	number of times to evaluate profile likelihood

Details

The GPD approximation in the tail is used to estimate quantile. The "wald" method uses the observed Fisher information matrix to calculate confidence interval. The "likelihood" method reparametrizes the likelihood in terms of the unknown quantile and uses profile likelihood arguments to construct a confidence interval.

See Also

gpd, plot.gpd, gpd.sfall, tailplot

Examples

## Not run: data(danish)
## Not run: out <- gpd(danish, 10) 
## Not run: tp <- tailplot(out) 
## Not run: gpd.q(tp, 0.999) 
# Estimates 99.9th percentile of Danish fire losses 

Example output

evir documentation built on May 2, 2019, 12:21 p.m.